Nicolas Ogonosky

Devising spectrum of tests for different types of autism

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Eva Loth

Lecturer in translational neurodevelopment, King’s College London

The Expert:

Eva Loth

Lecturer in translational neurodevelopment, King’s College London

One of the biggest challenges in studying autism is the condition’s heterogeneity. By definition, each person with autism has difficulties interacting and communicating with others and engages in repetitive and restricted behaviors. But the nature and severity of these features vary significantly. This diversity represents a major hurdle for developing treatments for individuals on the spectrum.

Most studies ignore this diversity and instead focus on what makes people with autism different from a ‘neurotypical’ control group. No single psychological or neurobiological feature has emerged that characterizes all people with autism1. Rather, there appear to be distinct subtypes of the condition that vary in their cognitive profile, underlying biology and prognosis.

This variability means that subgroups of people with autism may need different treatments. A certain treatment may be effective for a subtype of the condition, but clinical trials that include people of all subtypes may not pick up on its benefit.

In order to identify individuals with different subtypes of autism, we need ‘stratification biomarkers.’ These biomarkers could be biological features, such as atypical brain development or function, detected with, for example, electroencephalography, which picks up brain waves using electrodes attached to scalp. They could also be behaviors, such as atypical gaze behavior, as measured by eye-tracking technology.

Digging deep:

Several teams have tried to identify biomarkers for autism, but most of the studies have involved fewer than 30 participants per group. If researchers were to split each of these groups into two or more subgroups, they would quickly lose the statistical power to detect any effects.

We designed the Longitudinal European Autism Project to sidestep this pitfall. The project, which is part of the European Union Autism Innovative Medicine Initiative (EU-AIMS) consortium, is the largest multidisciplinary study on autism to date.

The study includes more than 350 people with autism spectrum disorder, 250 children and adults with typical development or mild intellectual impairments, and 20 typically developing twin pairs; the participants range in age from 6 to 30 years. It also includes more than 50 twin pairs in which one sibling has autism, helping us to better tease apart genetic and environmental factors.

In this project, we thoroughly assess each participant’s autism symptoms, quality of life, daily living skills and cognitive function. We also perform imaging to track brain development and function over time, and collect blood and saliva for genomic analyses. Through a tight collaboration between seven European universities, we were able to test this large sample in less than two years.

Neural signatures:

We are using two approaches to identify biomarkers for autism subtypes. In the first, we split participants into groups based on characteristics that are likely to reflect biological differences.

For example, some biomarkers may be specific to a certain gender or age range, or to people who have intellectual disabilities2. Neural signatures may also differ between people who have autism only and those who have both autism and conditions such as attention deficit hyperactivity disorder or depression.

In the second approach, we plan to use innovative techniques to discern certain subtypes from their cognitive profiles or patterns of brain connectivity3.

We are also excited to explore whether we can identify subtypes based on a person’s profile of genetic mutations, which cancer researchers have used to identify tumor subtypes4.

If these techniques enable us to systematically subdivide autism, we plan to compare the neurobiology and clinical symptoms of the resulting subgroups. This may reveal ‘final common pathways’ — shared features caused by different genetic or environmental factors.

Personalized medicine:

While designing the study, we planned for the phase that starts after the research is complete. We believe this should be a key component of studies aiming to find biomarkers for autism.

In particular, we took steps to smooth the way toward regulatory approval for these biomarkers. Early on, a team of academic and industry partners from EU-AIMS consulted with the European Medicines Agency — the European counterpart of the U.S. Food and Drug Administration — about parameters for our study that would enable us to convince regulators that our biomarkers are ready for use in clinical trials.

We asked for advice on whom to include in the study, which outcome measures to use and how best to identify biomarkers. We published the outcome of these discussions on 31 December in Nature Reviews Drug Discovery5.

We hope that the findings from our study will bring us closer to developing stratified or personalized therapies for autism. After all, autism is not one thing. We need to find treatments or interventions that best address the specific needs of each individual on the spectrum.

Eva Loth is Sackler Lecturer in Translational Neurodevelopment at the Institute of Psychiatry, King’s College London, and EU-AIMS project science coordinator and deputy lead clinical researcher.

Yes, the children I see with autism are so different as to make the diagnosis almost meaningless. And the problems with friendships may be telling us more about neurotypical children’s dislike of anything outside a norm, their pack values, than the umbrella autistic group. Glad you are looking at serum, but again, the very anxious children inevitably are excluded from researxh, and that’s a large sub group.

Mark Carew

Autism aside , using statistics to resolve a complex problem generally produces more complexity. Your technique appears to have wanted to rationalize to sub groups, whereas a single identifiable subgroup will provide the common pathway you seek. This would best targeted at the easiest to research in the first instance. Like disentangling multiple strands of wool, start with the brightest colour or the shortest length. The trickiest one remains at the end disentangled. Beware of knots which may show as multiple type!

Sandra Barwick

Good point. The University of Missouri some years ago identified as the largest sub group, 25 per cent, those who were anxious with various sensitivities (light,noise etc) and also had gut problems. That would be the place to start, I think.

Shree

Best wishes for your work on autism. Diagnosis of autism is complex in nature. Hope your research and hard work on it will contribute to the field of it.Thank you.

Seth Bittker

The Longitudinal European Autism Project mentioned above sounds promising. I understand that it will include collection of “blood and saliva for genomic analyses”. I think it would be wonderful if this was extended to metabolomic analysis as autism often features a characteristic biochemistry and that biochemistry is likely to be a product of a massive number of genetic and environmental factors and their history (epigenetics). So focusing on the major common elements of typical autism biochemistry while still complex is a simpler problem and in addition should give one greater insight into potential solutions.

Relatedly, the biomarkers mentioned above are macroscopic markers such as “atypical brain development or function” or abnormal “brain waves”. Why not look at biomarkers that are direct measures of biochemistry? There are many good candidates in the literature including low plasma sulfate, high nitro-tyrosine in blood, or a high ratio of oxidized to reduced glutathione. It seems to me using markers of biochemistry will naturally suggest what therapies could be helpful to a particular individual. For example, low sulfate in plasma suggests that sulfation agonists should be considered.

Kathleen McKeoghain

Is there a subtype for Asperger Syndrome? I am curious because I am a certain outlier.

I think the biggest challenge in research is the quantitative genetic architecture upon which epigenetics operates in the development of autism. We are still dependent upon defined and isolated data “threads” in research, by our proper statistical models we must be, yet our genomes work in extraordinary ways that defy any whole genome modeling to date.

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